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All Journal TEKNIK INFORMATIKA Sainteks Jurnal Informatika Upgris Journal of Advanced Civil and Environmental Engineering Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat

Khoiriya Latifah, Khoiriya

Universitas PGRI Semarang

Author-ID : 556621

Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Environmental Science Mechanical Engineering Medicine & Pharmacology Other

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IDENTIFIKASI SERAT BAMBU MENGGUNAKAN EKSTRAKSI CIRI STATISTIK ORDE 2 (GLCM) DAN PENGUKURAN JARAK K-NN Khoiriya Latifah; Abdul Rochim; Bambang Supriyadi
JURNAL TEKNIK INFORMATIKA Vol 12, No 2 (2019): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Indonesia is a large bamboo producer. Many benefits can be taken from bamboo trees, among others, as an alternative material for environmentally friendly construction, handicraft, and even become a safe material for use. Based on the property of its mechanical strength, bamboo has high tensile strength and fiber content, including fiber length, inter-fiber adhesive, namely lignin and the higher diameter of bamboo fiber, causing bamboo stems to become stronger and stiffer so that bamboo quality is getting better. One objective is to use a texture analysis of statistical features extraction of digital image processing. Feature extraction is a process to get the characteristics of visual perception. Texture information can be used to distinguish the surface properties of objects in images that are related to coarse and fine. This research uses a second-order statistical calculation of Gray Level Co-occurrence Matrices (GLCM) by measuring contrast, energy, homogeneity, and correlation levels to determine roughness from bamboo image textures that have irregular patterns. The second method is to use similarity measurements with the K-NN method in which in this study K = 3 with testing images of 28 images obtained an accuracy of 0.8, precission of 0, 8 and f-measurement of 0.9.

ANALISIS DAN PENERAPAN ALGORITHMA C45 DALAM DATA MINING UNTUK MENUNJANG STRATEGI PROMOSI PRODI INFORMATIKA UPGRIS Khoiriya Latifah
JURNAL TEKNIK INFORMATIKA Vol 11, No 2 (2018): Jurnal Teknik Informatika
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

ABSTRAK Untuk menarik minat pendaftar mahasiswa baru memerlukan strategi khusus. Salah satu strategi adalah dengan melakukan analisa data dengan tujuan mengubah kumpulan data menjadi memiliki nilai bisnis melalui laporan analitik sehingga menghasilkan informasi yang akan diambil polanya menjadi pengetahuan [Kusrini, 2009]. Teknik klasifikasi merupakan pendekatan fungsi klasifikasi dalam data mining yang digunakan untuk melakukan prediksi atas informasi yang belum diketahui sebelumnya[Larose, 2005]. Pohon keputusan merupakan metode klasifikasi dan prediksi. pada penelitian ini algorithma yang dipakai untuk pembentukan pohon keputusan dengan mengunakan algoritma C45[Larose, 2005]. Data yang diproses adalah data mahasiswa baru angkatan 2014 dan angkatan 2015. Hasil penelitian ini menunjukkan bahwa variabel yang paling tinggi pengaruhnya terhadap hasil registrasi mahasiswa adalah Asal Sekolah dan Jenis Kelamin. Rata-rata berasal dari Semarang dengan jurusan SMU dari IPA dan yang berasal dari luar kota rata-rata berasal dari Batang dan Pati. Dari SMU jurusan IPS dan berjenis kelamin Laki-laki berasal dari Batang dan yang berjenis kelamin Perempuan berasal dari Pati.. Accuracy dari pembenukan model ini adalah sebesar 89.33 % (Good Classification). ABSTRACT To attract new student applicants requires a special strategy. One strategy is to perform data analysis with the aim of converting the data set to have business value through analytic reports so that the information will be taken into the pattern of knowledge [Kusrini, 2009]. The classification technique is an approximate classification function in data mining used to predict information previously unknown [Larose, 2005]. Decision tree is a method of classification and prediction. in this study the algorithm used for the formation of decision trees using the C45 algorithm [Larose, 2005]. Processed data are new student data of class of 2014 and class of 2015. The result of this research indicates that the variable that has the highest effect on student registration result is School Origin and Gender. The average comes from Semarang with high school majors from IPA and those coming from out of town on average come from Batang and Pati. Of SMU majoring in IPS and Male sex comes from the stem and the female sex is derived from Pati .. Accuracy of this model is 89.33% (Good Classification).

Co-Authors Abdul Rochim Agung Handayanto Bambang Agus Herlambang, Bambang Bambang Agus Herlambang, Bambang Agus Bambang Supriyadi Bambang Supriyadi Carsoni C Joko Siswanto Murti Dewanto, Febrian Noora Qotrun Nada Noora Qotrun Nada, Noora Qotrun Padmi Dhyah Yulianti Setyoningsih Wibowo Tri Jaka Harjanta, Aris Yuris Setyoadi

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